biglasso-package | R Documentation |
Extend lasso and elastic-net linear, logistic and cox regression models for ultrahigh-dimensional, multi-gigabyte data sets that cannot be loaded into available RAM. This package utilizes memory-mapped files to store the massive data on the disk and only read those into memory whenever necessary during model fitting. Moreover, some advanced feature screening rules are proposed and implemented to accelerate the model fitting. As a result, this package is much more memory- and computation-efficient and highly scalable as compared to existing lasso-fitting packages such as glmnet and ncvreg, thus allowing for powerful big data analysis even with only an ordinary laptop.
Package: | biglasso |
Type: | Package |
Version: | 1.4-1 |
Date: | 2021-01-29 |
License: | GPL-3 |
Penalized regression models, in particular the lasso, have been extensively applied to analyzing high-dimensional data sets. However, due to the memory limit, existing R packages are not capable of fitting lasso models for ultrahigh-dimensional, multi-gigabyte data sets which have been increasingly seen in many areas such as genetics, biomedical imaging, genome sequencing and high-frequency finance.
This package aims to fill the gap by extending lasso model fitting to Big
Data in R. Version >= 1.2-3 represents a major redesign where the source
code is converted into C++ (previously in C), and new feature screening
rules, as well as OpenMP parallel computing, are implemented. Some key
features of biglasso
are summarized as below:
it utilizes memory-mapped files to store the massive data on the disk, only loading data into memory when necessary during model fitting. Consequently, it's able to seamlessly data-larger-than-RAM cases.
it is built upon pathwise coordinate descent algorithm with warm start, active set cycling, and feature screening strategies, which has been proven to be one of fastest lasso solvers.
in incorporates our newly developed hybrid and adaptive screening that outperform state-of-the-art screening rules such as the sequential strong rule (SSR) and the sequential EDPP rule (SEDPP) with additional 1.5x to 4x speedup.
the implementation is designed to be as
memory-efficient as possible by eliminating extra copies of the data created
by other R packages, making it at least 2x more memory-efficient than
glmnet
.
the underlying computation is implemented in C++, and parallel computing with OpenMP is also supported.
For more information:
Benchmarking results: https://github.com/YaohuiZeng/biglasso.
Tutorial: http://yaohuizeng.github.io/biglasso/articles/biglasso.html
Technical paper: https://arxiv.org/abs/1701.05936
The input design matrix X must be a big.matrix
object.
This can be created by the function as.big.matrix
in the R package
bigmemory.
If the data (design matrix) is very large (e.g. 10 GB) and stored in an external
file, which is often the case for big data, X can be created by calling the
function setupX
.
In this case, there are several restrictions about the data file:
the data file must be a well-formated ASCII-file, with each row corresponding to an observation and each column a variable;
the data file must contain only one single type. Current version only
supports double
type;
the data file must contain only numeric variables. If there are categorical variables, the user needs to create dummy variables for each categorical varable (by adding additional columns).
Future versions will try to address these restrictions.
Denote the number of observations and variables be, respectively, n
and p
. It's worth noting that the package is more suitable for wide
data (ultrahigh-dimensional, p >> n
) as compared to long data
(n >> p
). This is because the model fitting algorithm takes advantage
of sparsity assumption of high-dimensional data. To just give the user some
ideas, below are some benchmarking results of the total computing time (in
seconds) for solving lasso-penalized linear regression along a sequence of
100 values of the tuning parameter. In all cases, assume 20 non-zero
coefficients equal +/- 2 in the true model. (Based on Version 1.2-3,
screening rule "SSR-BEDPP" is used)
For wide data case (p > n
), n = 1,000
:
p | 1,000 | 10,000 | 100,000 | 1,000,000 |
Size of X | 9.5 MB | 95 MB | 950 MB | 9.5 GB |
Elapsed time (s) | 0.11 | 0.83 | 8.47 | 85.50 |
Yaohui Zeng, Chuyi Wang and Patrick Breheny
Maintainer: Yaohui Zeng <yaohui.zeng@gmail.com> and Chuyi Wang <wwaa0208@gmail.com>
Zeng, Y., and Breheny, P. (2017). The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R. https://arxiv.org/abs/1701.05936.
Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J., and Tibshirani, R. J. (2012). Strong rules for discarding predictors in lasso-type problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(2), 245-266.
Wang, J., Zhou, J., Wonka, P., and Ye, J. (2013). Lasso screening rules via dual polytope projection. In Advances in Neural Information Processing Systems, pp. 1070-1078.
Xiang, Z. J., and Ramadge, P. J. (2012). Fast lasso screening tests based on correlations. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 2137-2140). IEEE.
Wang, J., Zhou, J., Liu, J., Wonka, P., and Ye, J. (2014). A safe screening rule for sparse logistic regression. In Advances in Neural Information Processing Systems, pp. 1053-1061.
## Not run: ## Example of reading data from external big data file, fit lasso model, ## and run cross validation in parallel # simulated design matrix, 1000 observations, 500,000 variables, ~ 5GB # there are 10 true variables with non-zero coefficient 2. xfname <- 'x_e3_5e5.txt' yfname <- 'y_e3_5e5.txt' # response vector time <- system.time( X <- setupX(xfname, sep = '\t') # create backing files (.bin, .desc) ) print(time) # ~ 7 minutes; this is just one-time operation dim(X) # the big.matrix then can be retrieved by its descriptor file (.desc) in any new R session. rm(X) xdesc <- 'x_e3_5e5.desc' X <- attach.big.matrix(xdesc) dim(X) y <- as.matrix(read.table(yfname, header = F)) time.fit <- system.time( fit <- biglasso(X, y, family = 'gaussian', screen = 'Hybrid') ) print(time.fit) # ~ 44 seconds for fitting a lasso model along the entire solution path # cross validation in parallel seed <- 1234 time.cvfit <- system.time( cvfit <- cv.biglasso(X, y, family = 'gaussian', screen = 'Hybrid', seed = seed, ncores = 4, nfolds = 10) ) print(time.cvfit) # ~ 3 minutes for 10-fold cross validation plot(cvfit) summary(cvfit) ## End(Not run)
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